📈 Series Temporales: ARIMA/SARIMA

Metodología Box-Jenkins • Dataset AirPassengers (1949-1960)
144 observaciones Interactivo Forecast 24M
RMSE
39.0
Error cuadrático medio
MAE
35.0
Error absoluto medio
MAPE
7.4%
Error porcentual
0.725
Coef. determinación
AIC
-406.0
Criterio Akaike
Modelo
SARIMA(1,1,0)(0,1,0)[12]
Mejor por AIC

📊 Comparativa de Modelos Candidatos

Modelo AIC RMSE
SARIMA(1,1,0)(0,1,0) -359.98 73.2
SARIMA(1,1,1)(0,1,1) -339.17 46.4
SARIMA(2,1,0)(1,1,0) -334.25 54.8
SARIMA(0,1,1)(0,1,1) -340.69 43.1
Autor: @TodoEconometria — Prof. Juan Marcelo Gutiérrez Miranda
Hash ID: 4e8d9b1a5f6e7c3d2b1a0f9e8d7c6b5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c

Referencias académicas:
• Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
• Hyndman, R.J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
• Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press.

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